图 书 馆
音频和音乐处理的分层贝叶斯模型Hierarchical Bayesian Models for Audio and Music Processing |
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| 课程网址: | http://videolectures.net/mbc07_cemgil_hbm/ |
| 主讲教师: | A. Taylan Cemgil |
| 开课单位: | 剑桥大学 |
| 开课时间: | 2007-12-29 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In recent years, there has been an increasing interest in statistical approaches and tools from machine learning for the analysis of audio and music signals, driven partially by applications in music information retrieval, computer aided music education and interactive music performance systems. The application of statistical techniques is quite natural: acoustical time series can be conveniently modelled using hierarchical signal models by incorporating prior knowledge from various sources: from physics or studies of human cognition and perception. Once a realistic hierarchical model is constructed, many audio processing tasks such as coding, restoration, transcription, separation, identification or resynthesis can be formulated consistently as Bayesian posterior inference problems. \\ In this talk, we will review recent advances in various signal models for audio and music signal analysis. In particular, factorial switching state space models, Gamma-Markov random fields will be discussed. Some models admit exact inference, otherwise efficient algorithms based on variational or stochastic approximation methods can be developed. We will illustrate applications on music transcription, tempo tracking, restoration and source separation applications. |
| 关 键 词: | 机器学习; 音乐信息检索; 分层信号模型 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-05-15：lxf |
| 阅读次数: | 74 |